Vertical Machine Unlearning: Selectively Removing Sensitive Information From Latent Feature Space

Recently, the enactment of privacy regulations has promoted the rise of machine unlearning paradigm. Most existing studies mainly focus on removing unwanted data samples from a learnt model. Yet we argue that they remove overmuch information of data samples from latent feature space, which is far beyond the sensitive feature scope that genuinely needs to be unlearned. In this paper, we investigate a vertical unlearning mode, aiming at removing only sensitive information from latent feature space. First, we introduce intuitive and formal definitions for this unlearning and show its orthogonal relationship with existing horizontal unlearning. Secondly, given the fact of lacking general solutions to vertical unlearning, we introduce a ground-breaking solution based on representation detachment, where the task-related information is encouraged to retain while the sensitive information is progressively forgotten. Thirdly, observing that some computation results during representation detachment are hard to obtain in practice, we propose an approximation with an upper bound to estimate it, with rigorous theoretical analysis. We validate our method by spanning several datasets and models with prevailing performance. We envision this work as a necessity for future machine unlearning system and an essential component of the latest privacy-related legislation.